I was named a Knight Fellow!
Who says we can't share?
This page will serve as a collection of resources for myself and others to use freely. Use the notes at your own risk, no promises that they're error-free or comprehensible to anyone but myself. 😀
Some software I've helped develop.
- OSoMeTweet - A Python package for pulling data from Twitter's V2 endpoints. Designed with academics in mind.
Social Media Stuff
Research that focuses on various aspects of social media. For the most part, misinformation, disinformation, social bots, bot detection, and similarly related topics.
- Bakshy, Messing, Adamic (2015) - Exposure to ideologically diverse news and opinion on Facebook
- Bollen, Mao, Zeng (2020) - Twitter mood predicts the stock market
- Creski (2020) - A Decade of Social Bot Detection
- Del Vicario, Bessi, Zollo, Petroni, Scala, Caldarelli, Stanley, and Quattrociocchi (2016) - The spreading of misinformation online
- Deutch (2020) - Tracking Facebook’s Election Misinformation "Super-Spreaders" (NewsGuard Special Report: Election Misinformation)
- Ferrara, Chang, Chen, Muric, Patel - Characterizing social media manipulation in the 2020 U.S. presidential election
- Ferrara, Varol, Davis, Menczer, Flammini (2016) - The rise of social bots
- Grinberg, Joseph, Friedland, Swire-Thompson, Lazer (2019) - Fake news on Twitter during the 2016 U.S. presidential election
- Kramer, Guillory, Hancock (2014) - Experimental evidence of massive-scale emotional contagion through social networks
- Memon & Carley (2020) - Characterizing COVID-19 Misinformation CommunitiesUsing a Novel Twitter Dataset
- Pei, Muchnik, Andrade Jr., Zheng, & Makse (2014) - Searching for superspreaders of information in real-world social media
- Shao, Ciampaglio, Varol, Yang, Flammini, Menczer (2018) - The spread of low-credibility content by social bots
- Vosoughi, Roy, Aral (2018) - The spread of true and false news online
Random Course Readings
These links are to notes on various literature encountered through my Ph.D. program at IU.
- Agar (2012) - Lively Science (Book, Ch. 1-2)
- Barabási and Albert (1999) - Emergence of Scaling in Random Networks
- Barthelemy (2014) - Scaling: lost in the smog
- Bernstein, Shore, Lazer (2018) - How intermittent breaks in interaction improve collective intelligence
- Bishop (2020) - How Scientists Can Stop Fooling Themselves Over Statistics
- Butts (2009) - Revisiting the Foundations of Network Analysis
- Christakis and Fowler (2010) - Social Network Sensors for Early Detection of Contagious Outbreaks
- Cristelli, Tacchela & Pietronero (2015) - The Heterogeneous Dynamics of Economic Complexity
- De Solla Price (1965) - Networks of Scientific Papers
- Domingos (2012) - A Few Useful Things to Know About Machine Learning
- Flake (1998) - The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation (pp. 1-8; 129-136)
- Helbing, Farkas, Vicsek (2000) - Simulating dynamical features of escape panic
- Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, Makse (2010) - Identification of influential spreaders in complex networks
- Kitchin (2014) - Big Data, new epistemologies andparadigm shifts
- Makse, Havlin & Stanley (1995) - Modeling Urban Growth Patterns
- Mantegna & Stanley (1995) - Scaling Behaviour in the Dynamics of an Economic Index
- Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch3: pp. 35-43)
- Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch5: pp. 57-77)
- McIntyre (2019) - The Scientific Attitude: Defending Science from Denial, Fraud, and Pseudoscience. (Intro & Ch. 1)
- Néda, Ravasz, Brecht, Vicsek & Barabási (2000) - The sound of many hands clapping
- Page - The Model Thinker: What You Need to Know to Make Data Work for You (Ch2: pp. 13-25)
- Radicchi, Fortunato, and Castellano (2008) - Universality of Citation Distributions: Toward an objective measure of scientific impact
- Salganik et al. (2020) - Measuring the predictability of life outcomes with a scientific mass collaboration
- Siever (1968) - Science: Observational, Experimental, Historical
- Song, Qu, Blumm, Barabasi (2010) - Limits of Predictability in Human Mobility
- Stanley et al. (1996) - Scaling behaviour in the growth of companies
- Weaver (1948) - Science and Complexity
- Winsberg (2019) - Computer Simulations in Science
This is a collection of single slide summaries that I (or others) presented for i709 Complex Systems on various topics.
You'll also find longer-form powerpoint presentations from when I was responsible for leading a presentation with another student.
- DeVerna (2020) - Economic Complexity (overview of a few papers)
- DeVerna (2020) - Generalized h-Index (Science of Science)
- DeVerna (2020) - Mobility Prediction and Travel Distance (Mobility)
- DeVerna (2020) - Modeling the Collective Motion of Escape Panic (Collective Motion)
- Aiyappa (2020) - Power Laws
- DeVerna (2020) - Scaling Laws and Mechanistic Insight (Science of Cities)
- DeVerna (2020) - Why Linear Regression and Power Law Distributions Don't Mix (Power Laws)
Some helpful links to student funding resources.
Other random things that might be useful in the future.